[Bug]: vLLM throws error when sampling from Cerebras GPT Models

[RylanSchaeffer]

Your current environment

The output of `python collect_env.py`

python -u collect_env.py 
Collecting environment information...
Traceback (most recent call last):
  File "/lfs/skampere1/0/rschaef/KoyejoLab-Pretraining-Inference-Compute-Exchange-Rate/collect_env.py", line 765, in <module>
    main()
  File "/lfs/skampere1/0/rschaef/KoyejoLab-Pretraining-Inference-Compute-Exchange-Rate/collect_env.py", line 744, in main
    output = get_pretty_env_info()
             ^^^^^^^^^^^^^^^^^^^^^
  File "/lfs/skampere1/0/rschaef/KoyejoLab-Pretraining-Inference-Compute-Exchange-Rate/collect_env.py", line 739, in get_pretty_env_info
    return pretty_str(get_env_info())
                      ^^^^^^^^^^^^^^
  File "/lfs/skampere1/0/rschaef/KoyejoLab-Pretraining-Inference-Compute-Exchange-Rate/collect_env.py", line 568, in get_env_info
    vllm_version = get_vllm_version()
                   ^^^^^^^^^^^^^^^^^^
  File "/lfs/skampere1/0/rschaef/KoyejoLab-Pretraining-Inference-Compute-Exchange-Rate/collect_env.py", line 273, in get_vllm_version
    from vllm import __version__, __version_tuple__
ImportError: cannot import name '__version_tuple__' from 'vllm' (/lfs/skampere1/0/rschaef/miniconda3/envs/llmonk/lib/python3.11/site-packages/vllm/__init__.py)

Model Input Dumps

No response

🐛 Describe the bug

vLLM throws an error when attempting to use Cerebras's models. Here is a minimal reproduction:

from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import destroy_model_parallel


model = LLM(model="cerebras/Cerebras-GPT-1.3B", dtype="bfloat16")

model_sampling_params = SamplingParams(
    n=1,
    temperature=1.0,
    max_tokens=64,
    seed=0,
)

output = model.generate(
    prompts=["Please continue the following sentence: The quick brown fox jumps "],
    sampling_params=model_sampling_params,
)

The error is: TypeError: 'NoneType' object is not iterable

It arises here:

    def _verify_embedding_mode(self) -> None:
        architectures = getattr(self.hf_config, "architectures", [])
        self.embedding_mode = any(
            ModelRegistry.is_embedding_model(arch) for arch in architectures)

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[DarkLight1337]

cerebras/Cerebras-GPT-1.3B doesn't have a valid config.json file, I think. It should have the architectures field like in cerebras/Cerebras-GPT-13B.

[DarkLight1337]

Although HF does keep mappings from model_type to architecture in https://github.com/huggingface/transformers/blob/5615a393691c81e00251e420c73e4d04c6fe22e5/src/transformers/models/auto/modeling_auto.py#L1564, it's not always clear which mapping should be used.

[DarkLight1337]

To solve this, I suggest you pass the architecture name explicitly via --hf-overrides.

[RylanSchaeffer]

@DarkLight1337 could you please explain how to modify my above minimal example?

When I try something like:

model = LLM(
    model="cerebras/Cerebras-GPT-1.3B",
    hf_overrides={"model_name_or_path": "cerebras/Cerebras-GPT-1.3B"},
    dtype="bfloat16",
)

I hit the error: `TypeError: EngineArgs.init() got an unexpected keyword argument 'hf_overrides'

[DarkLight1337]

What is your vLLM version? You might have to update it for this to be supported.

[RylanSchaeffer]

0.5.4

[DarkLight1337]

Yeah, pretty sure you need to update vLLM.

[RylanSchaeffer]

Is there any guarantee of backwards consistency? I've been generating data for a couple months and I need to make sure there's no distribution shift if I change the vllm version

[DarkLight1337]

hf_overrides is a new option that was only added recently. It doesn't change old behavior.

[RylanSchaeffer]

I've updated to 0.6.6.post1. Can you please now tell me how to correctly call Cerebras usinng the LLM() class in Python?

[RylanSchaeffer]

I'm currently trying:

model = LLM(
    model="cerebras/Cerebras-GPT-1.3B",
    hf_overrides={"architecture": "cerebras/Cerebras-GPT-1.3B"},
    dtype="bfloat16",
)

But this throws:

vllm/model_executor/models/registry.py", line 416, in inspect_model_cls
    for arch in architectures:
TypeError: 'NoneType' object is not iterable

[RylanSchaeffer]

To ask a related but separate follow up question, when I try:

model = LLM(
    model="cerebras/Cerebras-GPT-13B",
    hf_overrides={"architecture": "model_type"},
    dtype="bfloat16",
)

I receive the following error: ValueError: Model architectures ['GPT2Model'] are not supported for now.

Since I believe all of the Cerebras models are based on GPT2, what would you advise?

[DarkLight1337]

The "architecture" field should be class name of the model that's implemented in vLLM. In this case, it should be GPT2LMHeadModel as shown in the list of supported models.